The emergence of cloud computing has established a growing trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers and cloud computing also provide opportunities to help the grid with respect to robustness and load balancing. For instance, as data centers are major and yet flexible electric loads, they can be good candidates to offer ancillary services, such as voluntary load reduction, to a smart grid. Also, data centers may better stabilize the price of energy in the electricity markets, and at the same time reduce their electricity cost by exploiting the diversity in the price of electricity in the day-ahead and real-time electricity markets. Therefore, in this thesis, we investigate such potentials within an analytical profit maximization framework to determine whether (and how) participation in energy and ancillary service markets can be beneficial to data centers. This involves developing new mathematical models based on queuing theory to understand the trade-off between quality-of-service and power consumption in computer systems and data centers. The most fundamental property of our proposed queuing model is that it is not only accurate but also appropriate for optimization-based designs due to its convexity characteristics.

Based on such model, we then conduct joint optimization of data centers’ service rates and their demand bids to different electricity markets. After that, we will expend our analysis to provide a unified and comprehensive energy portfolio optimization framework for data centers in the area of smart grids. Specially, we study how utilizing one energy option may affect selecting other energy options. We show that, the use of on-site storage and the deployment of geographical workload distribution can particularly help data centers in utilizing high-risk energy choices, such as renewable generation.

In our analysis, we take into account service-level-agreements, risk management constraints, and also the statistical characteristics of the workload and the electricity prices. Using empirical data, our numerical studies show that data centers can increase their profit by adjusting their power consumption according to the real time circumstances of the power grid, and also benefit from participating in the Ancillary Services and Demand Response programs.